How to improve the aspect of ggplot histograms with log scales and discrete values

ggplot2, r

Solution

The first thing that comes to mind, is playing with the `binwidth`. But that doesn't give a great solution either:

ggplot(data, aes(x=dist)) +
  geom_histogram(binwidth=10) +
  scale_x_continuous(expand=c(0,0)) +
  scale_y_continuous(expand=c(0.015,0)) +
  theme_bw()

gives:

In this case it is probably better to use a density plot. However, when you use `scale_x_log10` you will get a warning message (`Removed 524 rows containing non-finite values (stat_density)`). This can be resolved by using a log plus one transformation.

The following code:

library(ggplot2)
library(scales)

ggplot(data, aes(x=dist)) +
  stat_density(aes(y=..count..), color="black", fill="blue", alpha=0.3) +
  scale_x_continuous(breaks=c(0,1,2,3,4,5,10,30,100,300,1000), trans="log1p", expand=c(0,0)) +
  scale_y_continuous(breaks=c(0,125,250,375,500,625,750), expand=c(0,0)) +
  theme_bw()

will give this result:

Problem

I am trying to improve the clarity and aspect of a histogram of discrete values which I need to represent with a log scale. Please consider the following MWE ``` set.seed(99) data <- data.frame(dist = as.integer(rlnorm(1000, sdlog = 2))) class(data$dist) ggplot(data, aes(x=dist)) + geom_histogram() ``` which produces and then ``` ggplot(data, aes(x=dist)) + geom_line() + scale_x_log10(breaks=c(1,2,3,4,5,10,100)) ``` which probably is even worse since now it gives the impression that the something is missing between "1" and "2", and also is not totally clear which bar has value "1" (bar is on the right of the tick) and which bar has value "2" (bar is on the left of the tick). I understand that technically ggplot provides the "right" visual answer for a log scale. Yet as observer I have some problem in understanding it. Is it possible to improve something? EDIT: This what happen when I applied Jaap solution to my real data Where do the dips between x=0 and x=1 and between x=1 and x=2 come from? My value are discrete, but then why the plot is also mapping x=1.5 and x=2.5?

Original source